Measurement, Simulation, and Analysis of the Mechanical Response of Railroad Track

Increased railroad traffic volumes, speeds, and axle loads have created a need to better measure track quality. Previous research has indicated that the vertical track deflection provides a meaningful indicator of track integrity. The measured deflection can be related to the bending stresses in the rail as well as characterize the mechanical response of the track. This investigation summarizes the simulation, analysis and development of a measurement system at the University of Nebraska (UNL) to measure vertical track deflection in real-time from a car moving at revenue speeds. The UNL system operates continuously over long distances and in revenue service. Using a camera and two line lasers, the system establishes three points of the rail shape beneath the loaded wheels and over a distance of 10 ft. The resulting rail shape can then be related to the actual bending stress in the rail and estimate the track support through beam theory. Finite element simulations are used to characterize the track response as related to the UNL measurement system. The results of field tests using bondable resistance strain gages illustrate the system’s capability of approximating the actual rail bending stresses under load.

Evaluating Measurement Invariance with Censored Ordinal Data: A Monte Carlo Comparison of Alternative Model Estimators and Scales of Measurement

Evaluations of measurement invariance provide essential construct validity evidence. However, the quality of such evidence is partly dependent upon the validity of the resulting statistical conclusions. The presence of Type I or Type II errors can render measurement invariance conclusions meaningless. The purpose of this study was to determine the effects of categorization and censoring on the behavior of the chi-square/likelihood ratio test statistic and two alternative fit indices (CFI and RMSEA) under the context of evaluating measurement invariance. Monte Carlo simulation was used to examine Type I error and power rates for the (a) overall test statistic/fit indices, and (b) change in test statistic/fit indices. Data were generated according to a multiple-group single-factor CFA model across 40 conditions that varied by sample size, strength of item factor loadings, and categorization thresholds. Seven different combinations of model estimators (ML, Yuan-Bentler scaled ML, and WLSMV) and specified measurement scales (continuous, censored, and categorical) were used to analyze each of the simulation conditions. As hypothesized, non-normality increased Type I error rates for the continuous scale of measurement and did not affect error rates for the categorical scale of measurement. Maximum likelihood estimation combined with a categorical scale of measurement resulted in more correct statistical conclusions than the other analysis combinations. For the continuous and censored scales of measurement, the Yuan-Bentler scaled ML resulted in more correct conclusions than normal-theory ML. The censored measurement scale did not offer any advantages over the continuous measurement scale. Comparing across fit statistics and indices, the chi-square-based test statistics were preferred over the alternative fit indices, and ΔRMSEA was preferred over ΔCFI. Results from this study should be used to inform the modeling decisions of applied researchers. However, no single analysis combination can be recommended for all situations. Therefore, it is essential that researchers consider the context and purpose of their analyses.